Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface.

Shi Qiu,Yan Qin,Ting Liang,Junjun Li,Mengdi Cong,Chun Wu

doi:10.1155/2020/4930972

Abstract

Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

Highlights

Pulmonary nodules are the main pulmonary lesions
In view of the above problems and difficulties, in this paper, we propose a detection algorithm of solitary pulmonary nodules based on a brain-computer interface
All the data are marked by two professional doctors in accordance with the blind mark method, and disputed annotations are arbitrated by a third expert.10 groups of lung CT images are collected in the experiment, which include 3200 frames in total

Summary

Introduction

Pulmonary nodules are the main pulmonary lesions. Malignant pulmonary nodules may be transformed into lung cancer, which is a serious threat to human health [1, 2]. Kumar et al [14] extract features from EEG signals and apply them to the medical field. Liu et al [16] establish a correlation model between the P300 signal and time to realize BCI. Thomas et al [17] establish a deep learning network to realize BCI learning. Wang et al [24] propose an algorithm to enhance the useful signal strength of EEG. The main problems and difficulties of image recognition based on BCI are as follows: (1) Based on the brain structure, it is difficult for people to stay focused for a long time, making EEG information overlapping. In view of the above problems and difficulties, in this paper, we propose a detection algorithm of solitary pulmonary nodules based on a brain-computer interface. In view of the above problems and difficulties, in this paper, we propose a detection algorithm of solitary pulmonary nodules based on a brain-computer interface. (1) The time-frequency feature fusion model is constructed to enhance the signal identification. (2) A multifeature network based on deep learning is proposed. (3) Through the training of doctors and ordinary people, an effective response mechanism is proposed

Algorithm

Data Acquisition and Processing

Experiment and Result Analysis

Conclusion